chore: Refactor Python Codebase

metadata-ingestion-modules/airflow-plugin/setup.py

@@ -1,4 +1,5 @@
 import os
+import pathlib
 import sys
 from typing import Dict, Set
 
@@ -14,14 +15,11 @@
 
 def get_long_description():
     root = os.path.dirname(__file__)
-    with open(os.path.join(root, "README.md")) as f:
-        description = f.read()
-
-    return description
+    return pathlib.Path(os.path.join(root, "README.md")).read_text()
 
 
 base_requirements = {
-    # Compatability.
+    # Compatibility.
     "dataclasses>=0.6; python_version < '3.7'",
     "typing_extensions>=3.10.0.2",
     "mypy_extensions>=0.4.3",

metadata-ingestion-modules/airflow-plugin/src/datahub_airflow_plugin/datahub_plugin.py

@@ -1,3 +1,4 @@
+import contextlib
 import traceback
 from typing import Any, Iterable
 
@@ -215,7 +216,7 @@ def datahub_on_success_callback(context, *args, **kwargs):
     for inlet in inlets:
         datajob.inlets.append(inlet.urn)
 
-    # We have to use _oulets because outlets is empty
+    # We have to use _outlets because outlets is empty
     for outlet in task._outlets:
         datajob.outlets.append(outlet.urn)
 
@@ -389,13 +390,10 @@ def _patch_policy(settings):
 
 
 def _patch_datahub_policy():
-    try:
+    with contextlib.suppress(ImportError):
         import airflow_local_settings
 
         _patch_policy(airflow_local_settings)
-    except ImportError:
-        pass
-
     from airflow.models.dagbag import settings
 
     _patch_policy(settings)

metadata-ingestion-modules/airflow-plugin/tests/integration/integration_test_dummy.py

@@ -1,2 +1,2 @@
 def test_dummy():
-    assert True
+    pass

metadata-ingestion-modules/airflow-plugin/tests/unit/test_dummy.py

@@ -1,2 +1,2 @@
 def test_dummy():
-    assert True
+    pass

metadata-ingestion/examples/library/data_quality_mcpw_rest.py

@@ -47,7 +47,7 @@ def emitAssertionResult(assertionResult: AssertionRunEvent) -> None:
         aspect=assertionResult,
     )
 
-    # Emit BatchAssertion Result! (timseries aspect)
+    # Emit BatchAssertion Result! (timeseries aspect)
     emitter.emit_mcp(dataset_assertionRunEvent_mcp)
 
 

metadata-ingestion/examples/library/dataset_add_column_tag.py

@@ -23,18 +23,15 @@
 
 def get_simple_field_path_from_v2_field_path(field_path: str) -> str:
     """A helper function to extract simple . path notation from the v2 field path"""
-    if field_path.startswith("[version=2.0]"):
-        # this is a v2 field path
-        tokens = [
-            t
-            for t in field_path.split(".")
-            if not (t.startswith("[") or t.endswith("]"))
-        ]
-        path = ".".join(tokens)
-        return path
-    else:
+    if not field_path.startswith("[version=2.0]"):
         # not a v2, we assume this is a simple path
         return field_path
+        # this is a v2 field path
+    tokens = [
+        t for t in field_path.split(".") if not (t.startswith("[") or t.endswith("]"))
+    ]
+
+    return ".".join(tokens)
 
 
 # Inputs -> the column, dataset and the tag to set

metadata-ingestion/examples/library/dataset_add_column_term.py

@@ -23,18 +23,15 @@
 
 def get_simple_field_path_from_v2_field_path(field_path: str) -> str:
     """A helper function to extract simple . path notation from the v2 field path"""
-    if field_path.startswith("[version=2.0]"):
-        # this is a v2 field path
-        tokens = [
-            t
-            for t in field_path.split(".")
-            if not (t.startswith("[") or t.endswith("]"))
-        ]
-        path = ".".join(tokens)
-        return path
-    else:
+    if not field_path.startswith("[version=2.0]"):
         # not a v2, we assume this is a simple path
         return field_path
+        # this is a v2 field path
+    tokens = [
+        t for t in field_path.split(".") if not (t.startswith("[") or t.endswith("]"))
+    ]
+
+    return ".".join(tokens)
 
 
 # Inputs -> the column, dataset and the term to set

metadata-ingestion/examples/library/lineage_emitter_mcpw_rest.py

@@ -10,13 +10,11 @@
 )
 from datahub.metadata.schema_classes import ChangeTypeClass
 
-# Construct upstream tables.
-upstream_tables: List[UpstreamClass] = []
 upstream_table_1 = UpstreamClass(
     dataset=builder.make_dataset_urn("bigquery", "upstream_table_1", "PROD"),
     type=DatasetLineageTypeClass.TRANSFORMED,
 )
-upstream_tables.append(upstream_table_1)
+upstream_tables: List[UpstreamClass] = [upstream_table_1]
 upstream_table_2 = UpstreamClass(
     dataset=builder.make_dataset_urn("bigquery", "upstream_table_2", "PROD"),
     type=DatasetLineageTypeClass.TRANSFORMED,

metadata-ingestion/examples/library/lineage_job_dataflow_new_api_simple.py

@@ -1,5 +1,5 @@
 import uuid
-from datetime import datetime
+from datetime import datetime, timezone
 
 from datahub.api.entities.datajob import DataFlow, DataJob
 from datahub.api.entities.dataprocess.dataprocess_instance import (
@@ -36,40 +36,61 @@
 jobFlowRun = DataProcessInstance.from_dataflow(
     dataflow=jobFlow, id=f"{jobFlow.id}-{uuid.uuid4()}"
 )
-jobFlowRun.emit_process_start(emitter, int(datetime.utcnow().timestamp() * 1000))
+jobFlowRun.emit_process_start(
+    emitter, int(datetime.now(timezone.utc).timestamp() * 1000)
+)
+
 
 jobRun = DataProcessInstance.from_datajob(
     datajob=dataJob, id=f"{jobFlow.id}-{uuid.uuid4()}"
 )
-jobRun.emit_process_start(emitter, int(datetime.utcnow().timestamp() * 1000))
+jobRun.emit_process_start(emitter, int(datetime.now(timezone.utc).timestamp() * 1000))
+
 jobRun.emit_process_end(
-    emitter, int(datetime.utcnow().timestamp() * 1000), result=InstanceRunResult.SUCCESS
+    emitter,
+    int(datetime.now(timezone.utc).timestamp() * 1000),
+    result=InstanceRunResult.SUCCESS,
 )
 
+
 job2Run = DataProcessInstance.from_datajob(
     datajob=dataJob2, id=f"{jobFlow.id}-{uuid.uuid4()}"
 )
-job2Run.emit_process_start(emitter, int(datetime.utcnow().timestamp() * 1000))
+job2Run.emit_process_start(emitter, int(datetime.now(timezone.utc).timestamp() * 1000))
+
 job2Run.emit_process_end(
-    emitter, int(datetime.utcnow().timestamp() * 1000), result=InstanceRunResult.SUCCESS
+    emitter,
+    int(datetime.now(timezone.utc).timestamp() * 1000),
+    result=InstanceRunResult.SUCCESS,
 )
 
+
 job3Run = DataProcessInstance.from_datajob(
     datajob=dataJob3, id=f"{jobFlow.id}-{uuid.uuid4()}"
 )
-job3Run.emit_process_start(emitter, int(datetime.utcnow().timestamp() * 1000))
+job3Run.emit_process_start(emitter, int(datetime.now(timezone.utc).timestamp() * 1000))
+
 job3Run.emit_process_end(
-    emitter, int(datetime.utcnow().timestamp() * 1000), result=InstanceRunResult.SUCCESS
+    emitter,
+    int(datetime.now(timezone.utc).timestamp() * 1000),
+    result=InstanceRunResult.SUCCESS,
 )
 
+
 job4Run = DataProcessInstance.from_datajob(
     datajob=dataJob4, id=f"{jobFlow.id}-{uuid.uuid4()}"
 )
-job4Run.emit_process_start(emitter, int(datetime.utcnow().timestamp() * 1000))
+job4Run.emit_process_start(emitter, int(datetime.now(timezone.utc).timestamp() * 1000))
+
 job4Run.emit_process_end(
-    emitter, int(datetime.utcnow().timestamp() * 1000), result=InstanceRunResult.SUCCESS
+    emitter,
+    int(datetime.now(timezone.utc).timestamp() * 1000),
+    result=InstanceRunResult.SUCCESS,
 )
 
+
 jobFlowRun.emit_process_end(
-    emitter, int(datetime.utcnow().timestamp() * 1000), result=InstanceRunResult.SUCCESS
+    emitter,
+    int(datetime.now(timezone.utc).timestamp() * 1000),
+    result=InstanceRunResult.SUCCESS,
 )

metadata-ingestion/examples/transforms/custom_transform_example.py

@@ -61,13 +61,10 @@ def transform_aspect(  # type: ignore
         assert aspect is None or isinstance(aspect, OwnershipClass)
 
         if owners_to_add:
-            ownership = (
-                aspect
-                if aspect
-                else OwnershipClass(
-                    owners=[],
-                )
+            ownership = aspect or OwnershipClass(
+                owners=[],
             )
+
             ownership.owners.extend(owners_to_add)
 
         return ownership

metadata-ingestion/scripts/avro_codegen.py

@@ -10,11 +10,8 @@
 
 
 def load_schema_file(schema_file: str) -> str:
-    with open(schema_file) as f:
-        raw_schema_text = f.read()
-
-    redo_spaces = json.dumps(json.loads(raw_schema_text), indent=2)
-    return redo_spaces
+    raw_schema_text = Path(schema_file).read_text()
+    return json.dumps(json.loads(raw_schema_text), indent=2)
 
 
 def merge_schemas(schemas: List[str]) -> str:

metadata-ingestion/src/datahub/api/entities/datajob/dataflow.py

@@ -142,7 +142,7 @@ def emit(
         """
         Emit the DataFlow entity to Datahub
 
-        :param emitter: Datahub Emitter to emit the proccess event
+        :param emitter: Datahub Emitter to emit the process event
         :param callback: (Optional[Callable[[Exception, str], None]]) the callback method for KafkaEmitter if it is used
         """
         for mcp in self.generate_mcp():

metadata-ingestion/src/datahub/api/entities/datajob/datajob.py

@@ -52,7 +52,7 @@ class DataJob:
         properties Dict[str, str]: Custom properties to set for the DataProcessInstance
         url (Optional[str]): Url which points to the DataJob at the orchestrator
         inlets (List[str]): List of urns the DataProcessInstance consumes
-        outlest (List[str]): List of urns the DataProcessInstance produces
+        outlets (List[str]): List of urns the DataProcessInstance produces
         input_datajob_urns: List[DataJobUrn] = field(default_factory=list)
     """
 
@@ -179,7 +179,7 @@ def emit(
         """
         Emit the DataJob entity to Datahub
 
-        :param emitter: Datahub Emitter to emit the proccess event
+        :param emitter: Datahub Emitter to emit the process event
         :param callback: (Optional[Callable[[Exception, str], None]]) the callback method for KafkaEmitter if it is used
         :rtype: None
         """

metadata-ingestion/src/datahub/api/entities/dataprocess/dataprocess_instance.py

@@ -55,7 +55,7 @@ class DataProcessInstance:
         template_urn (Optional[Union[DataJobUrn, DataFlowUrn]]): The parent DataJob or DataFlow which was instantiated if applicable
         parent_instance (Optional[DataProcessInstanceUrn]): The parent execution's urn if applicable
         properties Dict[str, str]: Custom properties to set for the DataProcessInstance
-        url (Optional[str]): Url which points to the exection at the orchestrator
+        url (Optional[str]): Url which points to the execution at the orchestrator
         inlets (List[str]): List of entities the DataProcessInstance consumes
         outlets (List[str]): List of entities the DataProcessInstance produces
     """
@@ -118,10 +118,10 @@ def emit_process_start(
         """
 
         :rtype: None
-        :param emitter: Datahub Emitter to emit the proccess event
+        :param emitter: Datahub Emitter to emit the process event
         :param start_timestamp_millis: (int) the execution start time in milliseconds
         :param attempt: the number of attempt of the execution with the same execution id
-        :param emit_template: (bool) If it is set the template of the execution (datajob, datflow) will be emitted as well.
+        :param emit_template: (bool) If it is set the template of the execution (datajob, dataflow) will be emitted as well.
         :param callback: (Optional[Callable[[Exception, str], None]]) the callback method for KafkaEmitter if it is used
         """
         if emit_template and self.template_urn is not None:
@@ -312,8 +312,8 @@ def from_datajob(
 
         :param datajob: (DataJob) the datajob from generate the DataProcessInstance
         :param id: (str) the id for the DataProcessInstance
-        :param clone_inlets: (bool) wheather to clone datajob's inlets
-        :param clone_outlets: (bool) wheather to clone datajob's outlets
+        :param clone_inlets: (bool) whether to clone datajob's inlets
+        :param clone_outlets: (bool) whether to clone datajob's outlets
         :return: DataProcessInstance
         """
         dpi: DataProcessInstance = DataProcessInstance(